Basic Concepts of Inferential statistics

Basic Concepts of Inferential Statistics

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WHAT IS INFERENTIAL STATISTICS?

Inferential statistics is a technique used to draw conclusions about a population by testing the data taken from the sample of that population.

It is the process of how generalization from sample to population can be made. It is assumed that the characteristics of a sample is similar to the population’s characteristics.

It includes testing hypothesis and deriving estimates.

It focuses on making statements about the population.


THE PROCESS OF INFERENTIAL ANALYSIS

Raw

Data

• It comprises of all the data collected from the sample.

• Depending on the sample size, this data can be large or small set of measurements.

Sampl

e Statistics

• It summarizes the raw data gathered from the sample of population

• These are the descriptive statistics (e.g. measures of central tendency)

Inferential

Statistics

• These statistics then generate conclusions about the population based on the sample statistics.


SAMPLING METHODS

Random sampling is the best type of sampling method to use with inferential statistics. It is also referred to as probability sampling.

In this method, each participant has an equal probability of being selected in the sample.

In case the population is small enough then everyone can be used as a participant.

Another sampling technique is Snowball sampling which is a non-probability sampling.

Snowball sampling involves selecting participants on the basis of information provided by previously studied cases. This technique is not applied for inferential statistics.


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IMPORTANT DEFINITIONS

Probability is the mathematical possibility that a certain event will take place. They can range from 0 to 1.00

Parameters describe the characteristics of a sample of population. (Variables such as age, gender, income, etc.).

Statistics describe the characteristics of a sample on the same types of variables.

Sampling Distribution is used to make inferences based on the assumption of random sampling.


SAMPLING ERROR CONCEPTS

Sampling Error: Inferential statistics takes sampling error (random error) into account. It is the degree to which a sample differs on a key variable from the population.

Confidence Level: The number of times out of 100 that the true value will fall within the confidence interval.

Confidence Interval:A calculated range for the true value, based on the relative sizes of the sample and the population.

Sampling error describes the difference between sample statistics and population parameters.


SAMPLING DISTRIBUTION CONCEPTS

The variables of a sample taken from

the population

should be the same for the population

also.

Due to sampling error, the

sample mean can be varied.

The amount of this

variation in the sample

mean is referred to as

standard error.

Standard error

decreases as the sample

size increases.


TYPES OF HYPOTHESES

Alternative hypothesis: It specifies expected relationship between two or more variables. It may be symbolized by H1 or Ha.

Null hypothesis: It is the statement that says there is no real relationship between the variables described in the alternative hypothesis.

In inferential statistics, the hypothesis that is actually tested is the null hypothesis. Therefore, it is essential to prove that the null hypothesis is not valid and alternative hypothesis is true and should be accepted.


HYPOTHESIS TESTING PROCESS

State the research

hypothesis

State the null hypothesis

Choose a level of statistical significance

Select and compute the test statistic

Make a decision regarding whether to

accept or reject the null

hypothesis.




Basic Concepts of Inferential statistics

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